Legal claims defining the scope of protection. Each claim is shown in both the original legal language and a plain English translation.
1. A method of measuring an angle and an angular velocity between a first portion of a moving object and a second portion of the moving object comprising: (i) generating, using a light detection and ranging (LiDAR) device or a scanning laser rangefinder, a point cloud representing information on a relative position of a front edge of the second portion with respect to the first portion; (ii) calculating an angle between the first portion and the second portion from the point cloud; (iii) repeating steps (i) and (ii) to generate a plurality of calculated angles; (iv) calculating a weighted moving average from the plurality of calculated angles, wherein a later calculated angle of the plurality of calculated angles has a higher weight than an earlier calculated angle of the plurality of calculated angles; and (v) calculating an angular velocity from the plurality of calculated angles.
2. The method according to claim 1 wherein calculating the angle between the first portion and the second portion comprises filtering the point cloud with a predetermined threshold range.
3. The method according to claim 2 wherein points of the point cloud located outside the predetermined threshold range of distances are filtered out.
4. The method according to claim 3 wherein the predetermined threshold range is between 1 meter and 2.5 meters.
Technical Summary: This invention relates to a method for controlling a vehicle's speed based on proximity to a detected object, particularly in scenarios where the vehicle is approaching a stationary or slow-moving obstacle. The method addresses the problem of ensuring safe and efficient vehicle operation by dynamically adjusting speed to maintain a safe distance from detected objects, preventing collisions while optimizing traffic flow. The method involves detecting an object in the vehicle's path using sensors, such as radar or lidar, and determining the distance to the object. If the detected distance falls within a predetermined threshold range, the vehicle's speed is automatically adjusted to maintain a safe following distance. The threshold range is defined as between 1 meter and 2.5 meters, ensuring the vehicle responds appropriately to nearby obstacles while avoiding unnecessary braking or acceleration. The method may also include additional steps such as calculating the relative speed between the vehicle and the detected object, predicting potential collision risks, and adjusting the vehicle's speed accordingly. The system may further integrate with adaptive cruise control (ACC) or autonomous driving systems to enhance safety and driver assistance features. By dynamically adjusting speed within the specified threshold range, the invention improves vehicle safety and responsiveness in various driving conditions.
5. The method according to claim 1 wherein calculating the angle between the first portion and the second portion comprises performing coordinate change on the point cloud.
6. The method according to claim 5 wherein performing coordinate change comprises turning the point cloud from polar coordinate into Cartesian coordinate.
7. The method according to claim 1 wherein calculating the angle between the first portion and the second portion comprises performing linear fitting on the point cloud.
8. The method according to claim 7 wherein performing linear fitting comprises a least squares method.
A method for analyzing data involves performing linear fitting to determine a relationship between variables. The method is particularly useful in fields such as engineering, physics, or data science where linear relationships between variables need to be quantified. The problem addressed is the need for an accurate and efficient way to model linear relationships in datasets, which is essential for prediction, interpolation, and understanding underlying trends. The method includes collecting data points representing a relationship between two or more variables. These data points are then processed to perform linear fitting, which involves determining the best-fit line that minimizes the sum of squared differences between observed data points and the line. The linear fitting is performed using a least squares method, which is a mathematical technique that finds the line that minimizes the vertical distance between the data points and the line. This approach ensures that the fitted line provides the most accurate representation of the data, reducing errors and improving reliability. The method may also include preprocessing steps such as filtering or normalizing the data to improve the accuracy of the linear fitting. Additionally, the method may involve validating the fitted line by comparing it to known reference values or by assessing statistical measures such as the coefficient of determination (R-squared). The resulting linear model can then be used for predictive analysis, trend forecasting, or further data-driven decision-making. The use of the least squares method ensures that the fitting process is both computationally efficient and statistically robust, making it suitable for a wide range of applications.
9. The method according to claim 1 wherein weights of the plurality of calculated angles incrementally increase along with an order of the generation of the plurality of calculated angles.
10. The method according to claim 1 wherein the plurality of calculated angles are weighted linearly.
11. The method according to claim 1 wherein the angular velocity is calculated from the weighted moving average.
A system and method for calculating angular velocity in a rotating machinery monitoring application. The invention addresses the challenge of accurately determining angular velocity in dynamic environments where noise and transient disturbances can affect measurement reliability. The method involves processing sensor data from a rotating component to derive angular velocity using a weighted moving average technique. This approach applies variable weights to historical data points, emphasizing recent measurements while de-emphasizing older data, which improves responsiveness to sudden changes while maintaining stability. The weighted moving average calculation is performed on angular position data obtained from sensors monitoring the rotating component. The system may include multiple sensors positioned at different locations to provide redundant measurements, which are then fused to enhance accuracy. The method also incorporates error correction algorithms to compensate for sensor biases and environmental factors. The resulting angular velocity calculation is used for condition monitoring, fault detection, and predictive maintenance in industrial machinery. The invention is particularly useful in applications where traditional Fourier-based methods struggle with non-stationary signals or where real-time processing is required. The weighted moving average technique provides a balance between noise suppression and responsiveness, making it suitable for high-speed rotating equipment where rapid changes in velocity can occur.
12. A system for measuring an angle and an angular velocity between a first portion of a moving object and a second portion of the moving object, the system comprising: an internet server, comprising: an I/O port, configured to transmit and receive electrical signals to and from a client device; a memory; a processor; and one or more programs stored in the memory and configured for execution by the processor, the one or more programs comprising instructions for: (i) generating, using a light detection and ranging (LiDAR) device or a scanning laser rangefinder, a point cloud representing information on a relative position of a front edge of the second portion with respect to the first portion; (ii) calculating an angle between the first portion and the second portion from the point cloud; (iii) repeating steps (i) and (ii) to generate a plurality of calculated angles; (iv) calculating a weighted moving average from the plurality of calculated angles, wherein a later calculated angle of the plurality of calculated angles has a higher weight than an earlier calculated angle of the plurality of calculated angles; and (v) calculating an angular velocity from the plurality of calculated angles.
13. The system according to claim 12 wherein calculating the angle between the first portion and the second portion comprises filtering the point cloud with a predetermined threshold range.
14. The system according to claim 12 wherein calculating the angle between the first portion and the second portion comprises performing coordinate change on the point cloud.
15. The system according to claim 14 wherein performing coordinate change comprises turning the point cloud from polar coordinate into Cartesian coordinate.
16. The system according to claim 12 wherein calculating the angle between the first portion and the second portion comprises performing linear fitting on the point cloud.
17. An autonomous vehicle, comprising: the system according to claim 12 ; a first portion; a second portion towed by the first portion; and a LiDAR projecting beams from one of the first portion and the second portion toward the other one of the first portion and the second portion.
18. The system according to claim 12 wherein a scanning angle of the LiDAR is in a range of 0° to 120°.
A system for LiDAR-based sensing includes a LiDAR device configured to emit and receive light pulses to detect and measure distances to objects in an environment. The system processes the reflected light pulses to generate a three-dimensional point cloud representing the spatial distribution of objects. The LiDAR device is mounted on a rotating platform or mechanism that allows it to scan the environment in a scanning angle range of 0° to 120°. This wide-angle scanning capability enables the system to capture a broad field of view, improving coverage and detection of objects in the surrounding area. The system may also include additional components such as a processor for analyzing the point cloud data, a memory for storing the data, and an interface for transmitting the data to other systems. The scanning angle range of 0° to 120° allows the LiDAR to cover a large horizontal or vertical sector, enhancing situational awareness in applications such as autonomous vehicles, robotics, or environmental monitoring. The system may further incorporate calibration or adjustment mechanisms to optimize the scanning angle based on operational requirements or environmental conditions.
19. The system according to claim 12 wherein an angle step of a scanning of the LiDAR is 0.35°.
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March 2, 2021
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